Machine Learning is a hot data science field which allows computers to learn from data. The potential applications of machine learning are vast, ranging from spam filters on social networks to computer vision for self-driving cars.

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Photo by Odette Scharenborg on Twitter

2050 is the year.

The bots and Web crawlers in each well-educated town are overwhelmed. The capacity to learn a computer has reached new standards and the future will never be the same, as we know.

Learn the general structure of how to approach Machine Learning problems

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Photo by Kelly Sikkema on Unsplash

The Process

When people think of Machine Learning, they often think of a program that is taking in data and spitting out predictions and insights. The process of performing Machine Learning often requires many more steps before and after the predictive analytics.

We try to think of the Machine Learning process as:

1. Formulating a Question

What is it that we want to find out? How will we reach the success criteria that we set? Let’s say we are performing machine learning for a high-traffic fast-casual restaurant chain, and our goal is to improve the customer experience. We can serve this goal in many ways. When we’re thinking about creating a model, we have to narrow down to one measurable, specific task. For example, we might say we want to predict the wait times for customers’ food orders within 2 minutes, so that we can give them an accurate time estimate. …

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Image source: RDRR.IO

As a discipline, statistics has mostly developed in the past century. — in the context of data science and big data.This article focuses on the first step in any data science project: exploring the data. Exploratory data analysis, or EDA, is a comparatively new area of statistics. Classical statistics focused almost exclusively on inference, a sometimes complex set of procedures for drawing conclusions about large populations based on small samples.

In 1962, John W. Tukey called for a reformation of statistics in his seminal paper “The Future of Data Analysis”. …

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Let’s learn how to get data in and look at it.We’ll need to remember a few things about pandas. First, pandas is a library for data analysis. The powerful tool of pandas is the data frame, a tabular data structure with labeled rows and columns.

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Photo by NeONBRAND on Unsplash

One of the most common question asked by newcomer to coding , How do I remember everything while I’m learning ? When you just starting learn to code , it feel bit overwhelming.

8 tips for remembering everything, you are learning

1. Focus on understanding concepts and ideas

When it comes to learning a spoken language, you need to do more than just memorizing the words. you need to understand how the language works in practices. Similarly when you are learning how to code you need to understand the concepts and ideas — NOT just memorize the programing language. The basic concepts and algorithms of all programing languages are important to understand so that you can make connections across your learning. …

In machine learning, when working with model training and testing, we often need to save and restore the trained models in a file, to reuse them to compare the model with other models, and to deploy the model on to another place for new data. Data saving in a file is called Serialization, while data restoration is called Deserialization.

We are also interested in various data forms and sizes. Some datasets are easily trained i.e. they take less time to train but even with GPU the datasets whose size is huge (more than 1 GB or more ) will take very long to train on a local machine. …


Muhammad Zohaib

I’m Data Science student.I love to create, learn and share my skills. learning a new technology, brushing up on current skills or writing Data Science articles.

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